public static float[,] CalcGradientMap(float[,] residuals, Complex[,] psfCorrelation, Rectangle psfSize)
{
var yPadding = psfSize.YEnd;
var xPadding = psfSize.XEnd;
var resPadded = Pad(residuals, yPadding, xPadding);
var ResPAdded = FFT.Forward(resPadded, 1.0);
var B = Fourier2D.Multiply(ResPAdded, psfCorrelation);
var bPadded = FFT.BackwardFloat(B, (double)(B.GetLength(0) * B.GetLength(1)));
var bMap = RemovePadding(bPadded, yPadding, xPadding);
return(bMap);
}
public static void DebugILGPU()
{
var frequencies = FitsIO.ReadFrequencies(@"C:\dev\GitHub\p9-data\small\fits\simulation_point\freq.fits");
var uvw = FitsIO.ReadUVW(@"C:\dev\GitHub\p9-data\small\fits\simulation_point\uvw.fits");
var flags = new bool[uvw.GetLength(0), uvw.GetLength(1), frequencies.Length]; //completely unflagged dataset
double norm = 2.0;
var visibilities = FitsIO.ReadVisibilities(@"C:\dev\GitHub\p9-data\small\fits\simulation_point\vis.fits", uvw.GetLength(0), uvw.GetLength(1), frequencies.Length, norm);
var visibilitiesCount = visibilities.Length;
int gridSize = 256;
int subgridsize = 8;
int kernelSize = 4;
int max_nr_timesteps = 1024;
double cellSize = 1.0 / 3600.0 * PI / 180.0;
var c = new GriddingConstants(visibilitiesCount, gridSize, subgridsize, kernelSize, max_nr_timesteps, (float)cellSize, 1, 0.0f);
var watchTotal = new Stopwatch();
var watchForward = new Stopwatch();
var watchBackwards = new Stopwatch();
var watchDeconv = new Stopwatch();
watchTotal.Start();
var metadata = Partitioner.CreatePartition(c, uvw, frequencies);
var psfGrid = IDG.GridPSF(c, metadata, uvw, flags, frequencies);
var psf = FFT.Backward(psfGrid, c.VisibilitiesCount);
FFT.Shift(psf);
var psfCutDouble = CutImg(psf);
var psfCut = ToFloatImage(psfCutDouble);
FitsIO.Write(psfCut, "psfCut.fits");
var totalSize = new Rectangle(0, 0, gridSize, gridSize);
var imageSection = new Rectangle(0, 128, gridSize, gridSize);
var bMapCalculator = new PaddedConvolver(PSF.CalcPaddedFourierCorrelation(psfCut, totalSize), new Rectangle(0, 0, psfCut.GetLength(0), psfCut.GetLength(1)));
var fastCD = new FastSerialCD(totalSize, psfCut);
fastCD.ResetLipschitzMap(ToFloatImage(psf));
var gpuCD = new GPUSerialCD(totalSize, psfCut, 100);
var lambda = 0.5f * fastCD.MaxLipschitz;
var alpha = 0.8f;
var xImage = new float[gridSize, gridSize];
var residualVis = visibilities;
/*var truth = new double[gridSize, gridSize];
* truth[30, 30] = 1.0;
* truth[35, 36] = 1.5;
* var truthVis = IDG.ToVisibilities(c, metadata, truth, uvw, frequencies);
* visibilities = truthVis;
* var residualVis = truthVis;*/
for (int cycle = 0; cycle < 4; cycle++)
{
//FORWARD
watchForward.Start();
var dirtyGrid = IDG.Grid(c, metadata, residualVis, uvw, frequencies);
var dirtyImage = FFT.BackwardFloat(dirtyGrid, c.VisibilitiesCount);
FFT.Shift(dirtyImage);
FitsIO.Write(dirtyImage, "dirty_" + cycle + ".fits");
watchForward.Stop();
//DECONVOLVE
watchDeconv.Start();
bMapCalculator.ConvolveInPlace(dirtyImage);
FitsIO.Write(dirtyImage, "bMap_" + cycle + ".fits");
//var result = fastCD.Deconvolve(xImage, dirtyImage, lambda, alpha, 1000, 1e-4f);
var result = gpuCD.Deconvolve(xImage, dirtyImage, lambda, alpha, 1000, 1e-4f);
if (result.Converged)
{
Console.WriteLine("-----------------------------CONVERGED!!!!------------------------");
}
else
{
Console.WriteLine("-------------------------------not converged----------------------");
}
FitsIO.Write(xImage, "xImageGreedy" + cycle + ".fits");
FitsIO.Write(dirtyImage, "residualDebug_" + cycle + ".fits");
watchDeconv.Stop();
//BACKWARDS
watchBackwards.Start();
FFT.Shift(xImage);
var xGrid = FFT.Forward(xImage);
FFT.Shift(xImage);
var modelVis = IDG.DeGrid(c, metadata, xGrid, uvw, frequencies);
residualVis = Visibilities.Substract(visibilities, modelVis, flags);
watchBackwards.Stop();
var hello = FFT.Forward(xImage, 1.0);
hello = Common.Fourier2D.Multiply(hello, psfGrid);
var hImg = FFT.Backward(hello, (double)(128 * 128));
//FFT.Shift(hImg);
FitsIO.Write(hImg, "modelDirty_FFT.fits");
var imgRec = IDG.ToImage(c, metadata, modelVis, uvw, frequencies);
FitsIO.Write(imgRec, "modelDirty" + cycle + ".fits");
}
}
public static void DebugSimulatedApprox()
{
var frequencies = FitsIO.ReadFrequencies(@"C:\dev\GitHub\p9-data\small\fits\simulation_point\freq.fits");
var uvw = FitsIO.ReadUVW(@"C:\dev\GitHub\p9-data\small\fits\simulation_point\uvw.fits");
var flags = new bool[uvw.GetLength(0), uvw.GetLength(1), frequencies.Length]; //completely unflagged dataset
double norm = 2.0;
var visibilities = FitsIO.ReadVisibilities(@"C:\dev\GitHub\p9-data\small\fits\simulation_point\vis.fits", uvw.GetLength(0), uvw.GetLength(1), frequencies.Length, norm);
var visibilitiesCount = visibilities.Length;
int gridSize = 256;
int subgridsize = 8;
int kernelSize = 4;
int max_nr_timesteps = 1024;
double cellSize = 1.0 / 3600.0 * PI / 180.0;
var c = new GriddingConstants(visibilitiesCount, gridSize, subgridsize, kernelSize, max_nr_timesteps, (float)cellSize, 1, 0.0f);
var watchTotal = new Stopwatch();
var watchForward = new Stopwatch();
var watchBackwards = new Stopwatch();
var watchDeconv = new Stopwatch();
watchTotal.Start();
var metadata = Partitioner.CreatePartition(c, uvw, frequencies);
var psfGrid = IDG.GridPSF(c, metadata, uvw, flags, frequencies);
var psf = FFT.BackwardFloat(psfGrid, c.VisibilitiesCount);
FFT.Shift(psf);
var psfCut = PSF.Cut(psf);
FitsIO.Write(psfCut, "psfCut.fits");
var random = new Random(123);
var totalSize = new Rectangle(0, 0, gridSize, gridSize);
var bMapCalculator = new PaddedConvolver(PSF.CalcPaddedFourierCorrelation(psfCut, totalSize), new Rectangle(0, 0, psfCut.GetLength(0), psfCut.GetLength(1)));
var fastCD = new FastSerialCD(totalSize, psfCut);
//fastCD.ResetAMap(psf);
var lambda = 0.5f * fastCD.MaxLipschitz;
var alpha = 0.8f;
var approx = new ApproxParallel();
var approx2 = new ApproxFast(totalSize, psfCut, 4, 8, 0f, 0.25f, false, true);
var xImage = new float[gridSize, gridSize];
var residualVis = visibilities;
/*var truth = new double[gridSize, gridSize];
* truth[30, 30] = 1.0;
* truth[35, 36] = 1.5;
* var truthVis = IDG.ToVisibilities(c, metadata, truth, uvw, frequencies);
* visibilities = truthVis;
* var residualVis = truthVis;*/
var data = new ApproxFast.TestingData(new StreamWriter("approxConvergence.txt"));
for (int cycle = 0; cycle < 4; cycle++)
{
//FORWARD
watchForward.Start();
var dirtyGrid = IDG.Grid(c, metadata, residualVis, uvw, frequencies);
var dirtyImage = FFT.BackwardFloat(dirtyGrid, c.VisibilitiesCount);
FFT.Shift(dirtyImage);
FitsIO.Write(dirtyImage, "dirty_" + cycle + ".fits");
watchForward.Stop();
//DECONVOLVE
watchDeconv.Start();
//approx.ISTAStep(xImage, dirtyImage, psf, lambda, alpha);
//FitsIO.Write(xImage, "xIsta.fits");
//FitsIO.Write(dirtyImage, "dirtyFista.fits");
//bMapCalculator.ConvolveInPlace(dirtyImage);
//FitsIO.Write(dirtyImage, "bMap_" + cycle + ".fits");
//var result = fastCD.Deconvolve(xImage, dirtyImage, 0.5f * fastCD.MaxLipschitz, 0.8f, 1000, 1e-4f);
//var converged = approx.DeconvolveActiveSet(xImage, dirtyImage, psfCut, lambda, alpha, random, 8, 1, 1);
//var converged = approx.DeconvolveGreedy(xImage, dirtyImage, psfCut, lambda, alpha, random, 4, 4, 500);
//var converged = approx.DeconvolveApprox(xImage, dirtyImage, psfCut, lambda, alpha, random, 1, threads, 500, 1e-4f, cycle == 0);
approx2.DeconvolveTest(data, cycle, 0, xImage, dirtyImage, psfCut, psf, lambda, alpha, random, 10, 1e-4f);
if (data.converged)
{
Console.WriteLine("-----------------------------CONVERGED!!!!------------------------");
}
else
{
Console.WriteLine("-------------------------------not converged----------------------");
}
FitsIO.Write(xImage, "xImageApprox_" + cycle + ".fits");
watchDeconv.Stop();
//BACKWARDS
watchBackwards.Start();
FFT.Shift(xImage);
var xGrid = FFT.Forward(xImage);
FFT.Shift(xImage);
var modelVis = IDG.DeGrid(c, metadata, xGrid, uvw, frequencies);
residualVis = Visibilities.Substract(visibilities, modelVis, flags);
watchBackwards.Stop();
}
var dirtyGridCheck = IDG.Grid(c, metadata, residualVis, uvw, frequencies);
var dirtyCheck = FFT.Backward(dirtyGridCheck, c.VisibilitiesCount);
FFT.Shift(dirtyCheck);
var l2Penalty = Residuals.CalcPenalty(ToFloatImage(dirtyCheck));
var elasticPenalty = ElasticNet.CalcPenalty(xImage, (float)lambda, (float)alpha);
var sum = l2Penalty + elasticPenalty;
data.writer.Close();
}